Today's world envisions ‘personalization’. Hence, to digitize or automate a personalized service, knowledge harvesting about an individual person or entity is essential, which goes beyond the traditional data analytics and leads to the domain of Artificial Intelligence based Knowledge Management and Knowledge Harvesting. For e.g., in today's Retail industry, understanding the buying behavior of a customer about the products being purchased/searched is essential from business profitability perspectives, however is a complex problem, being influenced by several interdependent parameters.
Retailers around the world would leverage prior consumer purchase data to perform various data analytics and mining to derive meaningful/beneficial business parameters. Many of these analytics yield better performance when retailers would be able to track their revisiting customers. Retailers employ different techniques to track their repeating/revisiting customers such as: incentivizing customers who take part in loyalty membership programs, capturing a part of/or a digest of their payment instruments (like credit card, debit card etc.), providing faster self-check-out facilities that take only plastic instruments for payments, etc., so that a revisiting customer could be tracked and their transactions be tagged together. Modern Retail Systems and Web Applications facilitating such activities are thus primarily based on the ‘historic data’ of previous transactions and the current ‘Recommendation Systems’ orchestrate the designing methodologies comprising the ‘Content based filtering’ which captures products' features/capabilities correlations, the ‘Collaborative Filtering’ comprising similarity measure of consumers and/or products and a hybrid approach of the above two.